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An optimization method for spatial configuration of aircraft swarm based on self-adaptive Tent chaos search grey wolf optimizer is proposed for the optimal spatial configuration of aircraft swarm in the coordinated attack of fighters and unmanned aerial vehicles. A mathematical model of cooperative attack zone of air-to-air missiles is established, and a calculating method for cooperative attack zone under the condition of any aircraft swarm spatial configuration is proposed. The self-adaptive Tent chaos search is applied in grey wolf optimizer to improve the performance of the algorithm, thus avoiding the local optimum and the slow search speed. The size of cooperative attack zone is taken as the target function, and the spatial configuration of aircraft swarm is optimized by using the improved algorithm. The simulated results show that the improved algorithm can be used quickly to obtain the optimal spatial configuration with the largest cooperative attack zone and the minimal threat, and the cooperative attack zone of aircraft swarm is significantly larger than the sum of single aircraft attack zone under the optimal spatial configuration. [ABSTRACT FROM AUTHOR]

A hybrid adaptive genetic algorithm is proposed for the task scheduling of phased array radar. An optimal scheduling model for phased array radar is established. The performance and efficiency of the algorithm are improved by optimizing the initial population by the chaos theory, adopting the selection strategy of elite reservation and mixed ranking, and designing the adaptive crossover and mutation operators. A heuristic pulse interleaving algorithm is presented based on the adaptive genetic algorithm. It could utilize the waiting period in a task to execute the transmitting period or receiving period of other task. The simulated results demonstrate that the proposed algorithm provides better results and search solutions more quickly than the genetic algorithm. Moreover, compared with the heuristic scheduling algorithm , the proposed algorithm improves the scheduling success ratio, time utilization ratio and high value ratio, and decreases the average time shift ratio efficiently. [ABSTRACT FROM AUTHOR]

According to the fact that current condition evaluation models lay more emphasis on the static observations of characteristic parameters and less on trend information inherent in sequential observations, a new condition evaluation model with static and dynamic evaluations is proposed. For the weighting of multi-dimensional characteristic parameters, a mutative scale chaos algorithm is applied to achieve optimal objective weighting, and a static condition evaluation model is established. Based on static evaluation, a "distance" which measures real-time deteriorating speed and standard deteriorating speed is opted to modify the static evaluation result. NSD-WKFCM (neighbor sample density weighted kernel fuzzy C-means) clustering algorithm is used to solve standard vector of deteriorating speed. A dynamic adjusting function based on "distance" combined with its parameter optimization algorithm is proposed, and the complete dynamic condition evaluation model is established. A case study is performed to verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

To improve the efficiency of feature selection, a feature selection method based on chaos search (CSFS) is proposed. Firstly, a mapping model for feature candidates and chaotic variables is established, which maps the feature candidates to the chaos space and realizes the interconversion between them. Secondly, the feature selection is carried out by means of the evolution of the chaotic variable. Finally, the classifier is used to evaluate the obtained feature vector. The features of weld defects are taken for example to verify the proposed method, which is compared with a gene-algorithm-based feature selection ( GAFS) method. The experimental results demonstrate that the computation time of CSFS is only 61. 1% of that by GAFS in the case of obtaining the feature vectors with the same recognition performance. [ABSTRACT FROM AUTHOR]

The parameter estimation of chaotic system is a premise of system control and synchronization. In view of chaotic system's characteristics, such as sensitivity to initial condition, long-term unpredictability and so on, a filter applying to chaotic system was proposed based on chaotic system state space theory and particle filter (PF) theory. In a superimposed noise conditions, the parameter estimation and filtering of Lorenz chaotic system were simulated and analyzed. The simulation results show the proposed filtering algorithm is better than a chaotic system parameter estimation and filtering method based on extended Kalman filter (EKF) in bias estimates, and is an effective method for estimating the parameters of chaotic system and filter. [ABSTRACT FROM AUTHOR]